Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension: evaluation of robustness to contacts with blood vessels

Hidenobu Suzuki; Mikio Matsuhiro; Yoshiki Kawata; Toshihiko Sugiura; Nobuhiro Tanabe; Masahiko Kusumoto; Masahiro Kaneko; Noboru Niki

doi:10.1117/12.2612705

4 April 2022 Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension: evaluation of robustness to contacts with blood vessels

Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Toshihiko Sugiura, Nobuhiro Tanabe, Masahiko Kusumoto, Masahiro Kaneko, Noboru Niki

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203325 (2022) https://doi.org/10.1117/12.2612705
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Enlargement of the pulmonary artery is a morphological abnormality of pulmonary hypertension patients. Diameters of the aorta and main pulmonary artery (MPA) are useful for predicting the presence of pulmonary hypertension. A major problem in the automatic segmentation of the aorta and MPA from non-contrast CT images is the invisible boundary caused by contact with blood vessels. In this study, we applied U-Net to the segmentation of the aorta and MPA from non-contrast CT images for normal and chronic thromboembolic pulmonary hypertension (CTEPH) cases and evaluated the robustness to the contacts between blood vessels. Our approach of the segmentation consists of three steps: (1) detection of trachea branch point, (2) cropping region of interest centered to the trachea branch point, and (3) segmentation of the aorta and MPA using U-Net. The segmentation performances were compared in seven methods: 2D U-Net, 2D U-Net with pre-trained VGG-16 encoder, 2D U-Net with pre-trained VGG-19 encoder, 2D Attention U-Net, 3D U-Net, an ensemble method of them, and our conventional method. The aorta and MPA segmentation methods using these U-Net achieved higher performance than a conventional method. The contact boundaries of blood vessels caused lower performance compared with the non-contact boundaries, whereas the mean boundary distances were below about one pixel.

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Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Toshihiko Sugiura, Nobuhiro Tanabe, Masahiko Kusumoto, Masahiro Kaneko, and Noboru Niki "Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension: evaluation of robustness to contacts with blood vessels", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203325 (4 April 2022); https://doi.org/10.1117/12.2612705

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